Mismeasured or missing data can lead to false or misleading conclusions and present a widespread problem in a variety of biomedical fields, including environmental epidemiology. On the other hand, by carefully designing studies to make use of planned missingness, researchers may save money while achieving valid and powerful statistical inference. This project seeks to develop new statistical methods and to apply existing methods to address challenges to valid statistical estimation and testing posed by mismeasured or missing data, both unplanned and planned. Work continues on strategies for intentionally omitting some data to save on study costs while preserving most of the information that would have been available had all data been collected. When measuring an exposure is expensive (e.g., testing serum for toxic metabolites), assay cost may make certain exposures impractical to study. We showed that, in case-control studies, pooling samples from several individuals in a carefully designed way cuts assay costs substantially with negligible loss of statistical power.